packages = c('DT', 'dplyr', 'magrittr', 'kableExtra', 'ggplot2', 'plotly')

package.check <- lapply(
  packages,
  FUN = function(x) {
    if (!require(x, character.only = TRUE)) {
      install.packages(x, dependencies = TRUE)
      library(x, character.only = TRUE)
    }
  }
)

df <- c(1, 2, 3, 4, 5)

df <- data.frame(
  'x' = c(1, 2, 3, 4, 5),
  'y' = c(6, 7, 8, 9, 10)
)

df <- data.frame(
  'x' = c(1, 2, 3, 4, 5),
  'y' = c(6, 7, 8, 9, 10),
  'z' = c(11, 12, 13, 14, 15)
)

# DO NOT RUN

df <- data.frame(
  'good_name' = c(),
  'good-name1' = c(),
  'goodName' = c(),
  
  'bad name' = c(),
  'Bad.Name' = c(),
  'Really Bad Name' = c()
)

mean(df)

mean(df$x)

median(df)

median(df$x)

var(df)

var(df$x)

sd(df)

sd(df$x)

# Sort by frequency, descending order (child: mode())
sort_mode <- function(x) {
  temp <- data.frame(table(x))
  temp <- temp[order(-temp$Freq),]
  
  rownames(temp) <- NULL
  names(temp) <- c('value', 'frequency')
  
  return(temp)
}

# Find the mode (parent: sort_mode())
mode <- function(x) {
  x <- sort_mode(x)
  rows_x <- nrow(x)
  max_freq <- max(x$frequency)
  
  x$is_max <- 0
  x$is_max[x$frequency==max_freq] <- 1
  
  x <- x[x$is_max==1,]
  x <- data.frame(as.numeric(as.character(x$value)))
  rownames(x) <- NULL
  
  if(nrow(x)==rows_x){
    x = 'no mode'
  } else {
    names(x) <- c('mode(s):')
  }
  
  return(x)
}

mode(df)

mode(df$x)

se <- function(x) {
  temp <- round(sd(x)/sqrt(length(x)), digits=4)
  
  return(temp)
}

se(df)

se(df$x)

# Create table to manually calculate several statistics (r, r^2, a, and b)
make_table <- function(df) {
  require(DT)
  
  df$xy <- df$x*df$y
  df$x2 <- df$x**2
  df$y2 <- df$y**2
  
  all_sums <- c(sum(df$x), sum(df$y), sum(df$xy), sum(df$x2), sum(df$y2))
  df <- rbind(df, all_sums)
  rownames(df)[rownames(df)==as.character(nrow(df))] <- 'Total Sums'
  table <- DT::datatable(df,
    extensions = 'Buttons',
    caption = paste('Total observations: n = ', nrow(df)-1),
    options = list(
      dom = 'Bt',
      buttons = c('copy', 'csv', 'excel')
  ))
  return(table)
}

make_table(df)

df <- data.frame(
  'x' = c(1, 2, 3, 4, 5),
  'y' = c(0.1, 0.2, 0.3, 0.4, 0.5)
)

# Create table to manually calculate continuous probabilities
probability_table <- function(df) {
  df$mean <- df$x*df$y
  mean <- sum(df$mean)
  df <- subset(df, select=-c(mean))
  df$`x-m` <- df$x - mean
  df$`(x-m)^2` <- round(df$`x-m`**2, digits=3)
  df$`(x-m)^2*p(x)` <- round(df$`(x-m)^2`*df$y, digits=3)
  names(df)[names(df)=='y'] <- 'p(x)'
  
  var <- round(sum(df$`(x-m)^2*p(x)`), digits=3)
  sd <- round(sqrt(sum(df$`(x-m)^2*p(x)`)), digits=3)
  var_row <- c('', '', '', '', var)
  df <- rbind(df, var_row)
  sd_row <- c('', '', '', '', sd)
  df <- rbind(df, sd_row)
  rownames(df)[rownames(df)==as.character(nrow(df)-1)] <- 'Var.'
  rownames(df)[rownames(df)==as.character(nrow(df))] <- 'Std. Dev.'
  
  table <- DT::datatable(df,
    extensions = 'Buttons',
    caption = paste('See bottom rows for Variance (Var.) 
                    and Standard Deviation (Std. Dev.)'),
    options = list(
      dom = 'Bt',
      buttons = c('copy', 'csv', 'excel')
  ))
  return(table)
}

probability_table(df)

kableExtra::kbl(cbind(df, df)) %>%
  kableExtra::kable_paper() %>%
  kableExtra::scroll_box(width = "100%", height = "200px")

library(magrittr)

df %>%
  kableExtra::kbl(caption = 'Title') %>%
  kableExtra::kable_paper("hover")

library(magrittr)

df %>%
  kableExtra::kbl(caption = 'Title') %>%
  kableExtra::kable_paper("hover", full_width = F)

library(magrittr)

plotly::plot_ly(
  data = df,
  x = ~wt, 
  y = ~mpg,
  color = ~cyl)%>%
  plotly::layout(title='Title')

library(magrittr)

plotly::plot_ly(
  data = df,
  x = ~wt, 
  y = ~mpg,
  color = ~factor(cyl))%>%
  plotly::layout(title='Title')